Beyond One-Layer Decision: Hierarchical Multi-view Clustering with Progressive Cross-Layer Fusion

Deep multi-view clustering has demonstrated remarkable efficacy by leveraging the powerful feature extraction capabilities of autoencoders. However, existing autoencoder-based methods typically rely solely on final-layer embeddings of the encoder for downstream tasks, ignoring rich hierarchical information embedded in intermediate layers. To address this, we propose a novel Hierarchical-Aware Multi-view Clustering (HAMVC) framework that integrates complementarity across layers to promote intra-cluster compactness and inter-cluster separability. Specifically, we design a cross-layer affinity propagation module that implements a shallow-to-deep refinement strategy. By leveraging a multi-step diffusion mechanism, this module progressively integrates fine-grained structural patterns from shallow layers into semantic-rich deep features, establishing a robust progressive refinement path. Furthermore, to regulate interactions among heterogeneous views, we introduce a manifold-invariant diversity learning module. This module enforces a decentralized alignment strategy to capture the shared intrinsic manifold across views, while simultaneously imposing a diversity constraint to preserve view-specific complementary information and prevent representation collapse. Extensive experiments on several real-world datasets demonstrate that HAMVC achieves substantial improvements over state-of-the-art methods.